You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
221 lines
8.5 KiB
221 lines
8.5 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
|
|
import json
|
|
import signal
|
|
import sys
|
|
from pathlib import Path
|
|
from time import sleep, time
|
|
|
|
import requests
|
|
|
|
from ultralytics import __version__
|
|
from ultralytics.hub.utils import HUB_API_ROOT, check_dataset_disk_space, smart_request
|
|
from ultralytics.yolo.utils import is_colab, threaded, LOGGER, emojis, PREFIX
|
|
from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
|
|
|
|
AGENT_NAME = (f"python-{__version__}-colab" if is_colab() else f"python-{__version__}-local")
|
|
session = None
|
|
|
|
|
|
class HubTrainingSession:
|
|
|
|
def __init__(self, model_id, auth):
|
|
self.agent_id = None # identifies which instance is communicating with server
|
|
self.model_id = model_id
|
|
self.api_url = f"{HUB_API_ROOT}/v1/models/{model_id}"
|
|
self.auth_header = auth.get_auth_header()
|
|
self._rate_limits = {"metrics": 3.0, "ckpt": 900.0, "heartbeat": 300.0} # rate limits (seconds)
|
|
self._timers = {} # rate limit timers (seconds)
|
|
self._metrics_queue = {} # metrics queue
|
|
self.model = self._get_model()
|
|
self._start_heartbeat() # start heartbeats
|
|
self._register_signal_handlers()
|
|
|
|
def _register_signal_handlers(self):
|
|
signal.signal(signal.SIGTERM, self._handle_signal)
|
|
signal.signal(signal.SIGINT, self._handle_signal)
|
|
|
|
def _handle_signal(self, signum, frame):
|
|
"""
|
|
Prevent heartbeats from being sent on Colab after kill.
|
|
This method does not use frame, it is included as it is
|
|
passed by signal.
|
|
"""
|
|
if self.alive is True:
|
|
LOGGER.info(f"{PREFIX}Kill signal received! ❌")
|
|
self._stop_heartbeat()
|
|
sys.exit(signum)
|
|
|
|
def _stop_heartbeat(self):
|
|
"""End the heartbeat loop"""
|
|
self.alive = False
|
|
|
|
def upload_metrics(self):
|
|
payload = {"metrics": self._metrics_queue.copy(), "type": "metrics"}
|
|
smart_request(f"{self.api_url}", json=payload, headers=self.auth_header, code=2)
|
|
|
|
def upload_model(self, epoch, weights, is_best=False, map=0.0, final=False):
|
|
# Upload a model to HUB
|
|
file = None
|
|
if Path(weights).is_file():
|
|
with open(weights, "rb") as f:
|
|
file = f.read()
|
|
if final:
|
|
smart_request(
|
|
f"{self.api_url}/upload",
|
|
data={
|
|
"epoch": epoch,
|
|
"type": "final",
|
|
"map": map},
|
|
files={"best.pt": file},
|
|
headers=self.auth_header,
|
|
retry=10,
|
|
timeout=3600,
|
|
code=4,
|
|
)
|
|
else:
|
|
smart_request(
|
|
f"{self.api_url}/upload",
|
|
data={
|
|
"epoch": epoch,
|
|
"type": "epoch",
|
|
"isBest": bool(is_best)},
|
|
headers=self.auth_header,
|
|
files={"last.pt": file},
|
|
code=3,
|
|
)
|
|
|
|
def _get_model(self):
|
|
# Returns model from database by id
|
|
api_url = f"{HUB_API_ROOT}/v1/models/{self.model_id}"
|
|
headers = self.auth_header
|
|
|
|
try:
|
|
response = smart_request(api_url, method="get", headers=headers, thread=False, code=0)
|
|
data = response.json().get("data", None)
|
|
|
|
if data.get("status", None) == "trained":
|
|
raise ValueError(
|
|
emojis(f"Model trained. View model at https://hub.ultralytics.com/models/{self.model_id} 🚀"))
|
|
|
|
if not data.get("data", None):
|
|
raise ValueError("Dataset may still be processing. Please wait a minute and try again.") # RF fix
|
|
self.model_id = data["id"]
|
|
|
|
# TODO: restore when server keys when dataset URL and GPU train is working
|
|
|
|
self.train_args = {
|
|
"batch": data["batch_size"],
|
|
"epochs": data["epochs"],
|
|
"imgsz": data["imgsz"],
|
|
"patience": data["patience"],
|
|
"device": data["device"],
|
|
"cache": data["cache"],
|
|
"data": data["data"]}
|
|
|
|
self.input_file = data.get("cfg", data["weights"])
|
|
|
|
# hack for yolov5 cfg adds u
|
|
if "cfg" in data and "yolov5" in data["cfg"]:
|
|
self.input_file = data["cfg"].replace(".yaml", "u.yaml")
|
|
|
|
return data
|
|
except requests.exceptions.ConnectionError as e:
|
|
raise ConnectionRefusedError("ERROR: The HUB server is not online. Please try again later.") from e
|
|
except Exception:
|
|
raise
|
|
|
|
def check_disk_space(self):
|
|
if not check_dataset_disk_space(self.model["data"]):
|
|
raise MemoryError("Not enough disk space")
|
|
|
|
def register_callbacks(self, trainer):
|
|
trainer.add_callback("on_pretrain_routine_end", self.on_pretrain_routine_end)
|
|
trainer.add_callback("on_fit_epoch_end", self.on_fit_epoch_end)
|
|
trainer.add_callback("on_model_save", self.on_model_save)
|
|
trainer.add_callback("on_train_end", self.on_train_end)
|
|
|
|
def on_pretrain_routine_end(self, trainer):
|
|
"""
|
|
Start timer for upload rate limit.
|
|
This method does not use trainer. It is passed to all callbacks by default.
|
|
"""
|
|
# Start timer for upload rate limit
|
|
LOGGER.info(f"{PREFIX}View model at https://hub.ultralytics.com/models/{self.model_id} 🚀")
|
|
self._timers = {"metrics": time(), "ckpt": time()} # start timer on self.rate_limit
|
|
|
|
def on_fit_epoch_end(self, trainer):
|
|
# Upload metrics after val end
|
|
all_plots = {**trainer.label_loss_items(trainer.tloss, prefix="train"), **trainer.metrics}
|
|
|
|
if trainer.epoch == 0:
|
|
model_info = {
|
|
"model/parameters": get_num_params(trainer.model),
|
|
"model/GFLOPs": round(get_flops(trainer.model), 3),
|
|
"model/speed(ms)": round(trainer.validator.speed[1], 3)}
|
|
all_plots = {**all_plots, **model_info}
|
|
self._metrics_queue[trainer.epoch] = json.dumps(all_plots)
|
|
if time() - self._timers["metrics"] > self._rate_limits["metrics"]:
|
|
self.upload_metrics()
|
|
self._timers["metrics"] = time() # reset timer
|
|
self._metrics_queue = {} # reset queue
|
|
|
|
def on_model_save(self, trainer):
|
|
# Upload checkpoints with rate limiting
|
|
is_best = trainer.best_fitness == trainer.fitness
|
|
if time() - self._timers["ckpt"] > self._rate_limits["ckpt"]:
|
|
LOGGER.info(f"{PREFIX}Uploading checkpoint {self.model_id}")
|
|
self._upload_model(trainer.epoch, trainer.last, is_best)
|
|
self._timers["ckpt"] = time() # reset timer
|
|
|
|
def on_train_end(self, trainer):
|
|
# Upload final model and metrics with exponential standoff
|
|
LOGGER.info(f"{PREFIX}Training completed successfully ✅")
|
|
LOGGER.info(f"{PREFIX}Uploading final {self.model_id}")
|
|
|
|
# hack for fetching mAP
|
|
mAP = trainer.metrics.get("metrics/mAP50-95(B)", 0)
|
|
self._upload_model(trainer.epoch, trainer.best, map=mAP, final=True) # results[3] is mAP0.5:0.95
|
|
self.alive = False # stop heartbeats
|
|
LOGGER.info(f"{PREFIX}View model at https://hub.ultralytics.com/models/{self.model_id} 🚀")
|
|
|
|
def _upload_model(self, epoch, weights, is_best=False, map=0.0, final=False):
|
|
# Upload a model to HUB
|
|
file = None
|
|
if Path(weights).is_file():
|
|
with open(weights, "rb") as f:
|
|
file = f.read()
|
|
file_param = {"best.pt" if final else "last.pt": file}
|
|
endpoint = f"{self.api_url}/upload"
|
|
data = {"epoch": epoch}
|
|
if final:
|
|
data.update({"type": "final", "map": map})
|
|
else:
|
|
data.update({"type": "epoch", "isBest": bool(is_best)})
|
|
|
|
smart_request(
|
|
endpoint,
|
|
data=data,
|
|
files=file_param,
|
|
headers=self.auth_header,
|
|
retry=10 if final else None,
|
|
timeout=3600 if final else None,
|
|
code=4 if final else 3,
|
|
)
|
|
|
|
@threaded
|
|
def _start_heartbeat(self):
|
|
self.alive = True
|
|
while self.alive:
|
|
r = smart_request(
|
|
f"{HUB_API_ROOT}/v1/agent/heartbeat/models/{self.model_id}",
|
|
json={
|
|
"agent": AGENT_NAME,
|
|
"agentId": self.agent_id},
|
|
headers=self.auth_header,
|
|
retry=0,
|
|
code=5,
|
|
thread=False,
|
|
)
|
|
self.agent_id = r.json().get("data", {}).get("agentId", None)
|
|
sleep(self._rate_limits["heartbeat"])
|